Finite‐Time Robust Stabilization for Stochastic Neural Networks
Weixiong Jin, Xiaoyang Liu, Xiangjun Zhao, Nan Jiang, Zhengxin Wang · Abstract and Applied Analysis · 2012
This paper is concerned with the finite‐time stabilization for a class of stochastic neural networks (SNNs) with noise perturbations. The purpose of the addressed problem is to design a nonlinear stabilizator which can stabilize the states of neural networks in finite time. Compared with the previous references, a continuous stabilizator is designed to realize such stabilization objective. Based on the recent finite‐time stability theorem of stochastic nonlinear systems, sufficient conditions are established for ensuring the finite‐time stability of the dynamics of SNNs in probability. Then, the gain parameters of the finite‐time controller could be obtained by solving a linear matrix inequality and the robust finite‐time stabilization could also be guaranteed for SNNs with uncertain parameters. Finally, two numerical examples are given to illustrate the effectiveness of the proposed design method.